Polynomial Lyapunov Function Research Articles

Fault tolerant H ∞ control for a class of polynomial non-linear discrete-time systems

This paper studies the fault tolerant control (FTC) problem for a class of polynomial nonlinear discrete-time systems with guaranteed H∞ performance in the presence of actuator faults. The concerned fault is considered as a multi-model of the typical aberration in actuators’ effectiveness. A quadratic-like polynomial Lyapunov function is presented for the H∞ specification. The main contribution of this paper is that the effect of the nonlinear terms appear in FTC analysis is described as an index in order to transform the controller design into a semi-definite programming (SDP). A numerical example is given to verify the applicability of this new approach for the nonlinear FTC synthesis.

A Nonconservative LMI Condition for Stability of Switched Systems With Guaranteed Dwell Time

Ensuring stability of switched linear systems with a guaranteed dwell time is an important problem in control systems. Several methods have been proposed in the literature to address this problem, but unfortunately they provide sufficient conditions only. This technical note proposes the use of homogeneous polynomial Lyapunov functions in the non-restrictive case where all the subsystems are Hurwitz, showing that a sufficient condition can be provided in terms of an LMI feasibility test by exploiting a key representation of polynomials. Several properties are proved for this condition, in particular that it is also necessary for a sufficiently large degree of these functions. As a result, the proposed condition provides a sequence of upper bounds of the minimum dwell time that approximate it arbitrarily well. Some examples illustrate the proposed approach.

Global stability analysis of fluid flows using sum-of-squares

This paper introduces a new method for proving global stability of fluid flows through the construction of Lyapunov functionals. For finite dimensional approximations of fluid systems, we show how one can exploit recently developed optimization methods based on sum-of-squares decomposition to construct a polynomial Lyapunov function. We then show how these methods can be extended to infinite dimensional Navier–Stokes systems using robust optimization techniques. Crucially, this extension requires only the solution of infinite-dimensional linear eigenvalue problems and finite-dimensional sum-of-squares optimization problems.We further show that subject to minor technical constraints, a general polynomial Lyapunov function is always guaranteed to provide better results than the classical energy methods in determining a lower-bound on the maximum Reynolds number for which a flow is globally stable, if the flow does remain globally stable for Reynolds numbers at least slightly beyond the energy stability limit. Such polynomial functions can be searched for efficiently using the SOS technique we propose.

다항식 퍼지 대규모 시스템의 출력 궤환 제어기 설계 : 제곱합 접근 방법

본 논문에서는 출력 궤환 제어기를 이용해 다항식 퍼지 대규모 시스템을 안정화하는 방법을 제안한다. 이를 위해, 먼저, 대규모 시스템의 부분 시스템에 대해 퍼지 모델링을 한 후, 각각에 대해 출력 궤환 제어기를 설계하여 대규모 시스템을 안정화하는 방법을 제안한다. 이때, 다항식 Lyapunov 함수를 적용하면 다항식 퍼지 대규모 시스템의 안정화 조건은 제곱합 조건으로 주어지고, 이것은 MATLAB의 툴박스인 SOSTOOLS로 해석 가능하다. 이러한 과정을 거친 후에 얻어진 해에서 시스템을 안정화시키는 출력 궤환 제어기의 이득을 구할 수 있다. 마지막으로, 제안된 기법의 적합성을 검증하기 위해 시뮬레이션 예제가 주어진다. This paper presents the stabilization method for polynomial fuzzy large-scale system by using output feedback controller. Each sub system of the large-scale system is transformed into polynomial fuzzy model, and then output feedback controller is designed to stabilize the large-scale system. Stabilization condition is derived as sum-of-square (SOS) condition by applying the polynomial Lyapunov function. This condition can be easily solved by SOSTOOLS which is the third party of the MATLAB. From these solutions, output feedback controller gain can be obtained by SOS condition. Finally, a simulation example is presented to illustrate the effectiveness and the suitability of the proposed method.

Stability analysis of polynomial fuzzy models via polynomial fuzzy Lyapunov functions

In this paper, the stability of continuous-time polynomial fuzzy models by means of a polynomial generalization of fuzzy Lyapunov functions is studied. Fuzzy Lyapunov functions have been fruitfully used in the literature for local analysis of Takagi–Sugeno models, a particular class of the polynomial fuzzy ones. Based on a recent Taylor-series approach which allows a polynomial fuzzy model to exactly represent a nonlinear model in a compact set of the state space, it is shown that a refinement of the polynomial Lyapunov function so as to make it share the fuzzy structure of the model proves advantageous. Conditions thus obtained are tested via available SOS software.

On the robust stability of time‐varying uncertain genetic regulatory networks

SUMMARYThis paper investigates robust stability of time‐varying uncertain genetic regulatory networks (GRNs). In particular, the considered model includes, as special cases, SUM and PROD regulatory functions typically considered in the literature. It is supposed that the coefficients of the GRN are affine linear functions of an uncertain vector constrained in a polytope, and that the activation functions are uncertain into sector‐type regions. As the first problem, we consider to establish whether the GRN is robustly globally stable for all admissible uncertainties. It is shown that this problem can be addressed by solving a linear matrix inequality (LMI) feasibility test built by exploiting homogeneous polynomial Lyapunov functions. As the second problem, we consider to determine the slowest speed with which the concentrations of mRNAs and proteins reach their equilibrium values. It is shown that a guaranteed underestimate of such a speed can be provided by solving a generalized eigenvalue problem built from the proposed stability condition. Some numerical examples illustrate the proposed approaches. It is worth remarking that this paper proposes for the first time in the literature the use of nonquadratic Lyapunov functions for studying robust stability of uncertain GRNs, whereas existing works have addressed the problem only via quadratic Lyapunov functions (either common or parameter‐dependent), which are known to be conservative for time‐varying uncertainty. Copyright © 2011 John Wiley & Sons, Ltd.

LMI conditions for time-varying uncertain systems can be non-conservative

Establishing robust asymptotic stability of uncertain systems affected by time-varying uncertainty is a key problem. LMI sufficient conditions have been proposed in the literature for addressing this problem based on homogeneous polynomial Lyapunov functions. Unfortunately, till now it has been unclear whether these conditions are also necessary. This paper proposes a proof in order to show that one of these conditions is not only sufficient but also necessary for a sufficiently large degree of the Lyapunov function.

SOS Stability Conditions for Nonlinear Systems Based on a Polynomial Fuzzy Lyapunov Function

AbstractIn this paper, a new step on fuzzy relaxation for nonlinear systems’ stability analysis is addressed. Inspired from non-quadratic Lyapunov functions (NQLF), regarding to quadratic ones (QLF), a new polynomial fuzzy Lyapunov function (PFLF) is proposed as an extension to the polynomial Lyapunov function (PLF) [22]. Following the latter post-LMI challenge, the obtained stability conditions are written in terms of a sum-of-squares (SOS) optimization problem. The proposed PFLF includes the well-studied NQLF ones as a special case. Moreover, the proposed SOS based stability conditions don't require unknown parameters as well as guarantee, when a solution exists, the global stability.

Piecewise polynomial Lyapunov functions for global asymptotic stability of saturated uncertain systems

AbstractIn this paper we provide sufficient conditions for global asymptotic and global exponential stability of linear systems subject to saturations and/or deadzones. These conditions rely on piecewise polynomial Lyapunov functions and are formulated in terms of linear matrix inequalities, by using sum-of-squares relaxations. It is also shown that the proposed approach can be extended to deal with robust stability of saturated systems affected by structured parametric uncertainties. Example studies are presented to illustrate the reduced conservativeness of the proposed conditions as compared to existing results.

Robust Stability of Time-Varying Uncertain Systems With Rational Dependence on the Uncertainty

Robust stability of time-varying uncertain systems is a key problem in automatic control. This note considers the case of linear systems with rational dependence on an uncertain time-varying vector constrained in a polytope, which is typically addressed in the literature by using the linear fractional representation (LFR). A novel sufficient condition for robust stability is derived in terms of a linear matrix inequality (LMI) feasibility test by exploiting homogeneous polynomial Lyapunov functions, the square matrix representation and an extended version of Polya's theorem which considers structured matrix polynomials. It is shown that this condition is also necessary for second-order systems, and that this condition is less conservative than existing LMI conditions based on the LFR for any order.

Robust stability of uncertain piecewise-linear systems: LMI approach

In this paper we propose sufficient conditions for the robust stability of time-invariant uncertain piecewise-linear systems using homogeneous polynomial Lyapunov functions. The proposed conditions are expressed in terms of linear matrix inequalities, which can be numerically determined. We solve the stabilization of piecewise uncertain linear control systems by using state piecewise-linear feedback. We propose an illustrative example to show the efficiency of the proposed approach.

Simultaneous Stabilization and Robust Control of Polynomial Nonlinear Systems Using SOS Techniques

This technical note is concerned with the simultaneous stabilization and robust performance control for a class of polynomial nonlinear systems. Building on the tenets of state-dependent polynomial Lyapunov functions, we present sufficient conditions for simultaneous stabilization with and without Hinfin performance. These conditions can be verified by the recently developed sum of squares technique which essentially solves a linear matrix inequality feasibility problem.

On the Minimum Stable Commutation Time for Switching Nonlinear Systems

Real systems are often driven by switching reference signals which affect dynamics and/or equilibrium points. This technical note addresses the computation of upper bounds of the minimum commutation time ensuring stability for switching nonlinear systems. Specifically, we consider the cases of constant and variable equilibrium point of interest, for polynomial systems and for a class of non-polynomial systems. We hence propose upper bounds of the sought minimum commutation time by adopting homogeneous polynomial Lyapunov functions for the former case and polynomial Lyapunov functions for the latter one, which can be computed via linear matrix inequaltiy optimizations for given Lyapunov functions.

Exponentially Stable Nonlinear Systems Have Polynomial Lyapunov Functions on Bounded Regions

This paper presents a proof that existence of a polynomial Lyapunov function is necessary and sufficient for exponential stability of a sufficiently smooth nonlinear vector field on a bounded set. The main result states that if there exists an <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -times continuously differentiable Lyapunov function which proves exponential stability on a bounded subset of Rn, then there exists a polynomial Lyapunov function which proves exponential stability on the same region. Such a continuous Lyapunov function will exist if, for example, the vector field is at least <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -times continuously differentiable. The proof is based on a generalization of the Weierstrass approximation theorem to differentiable functions in several variables. Specifically, polynomials can be used to approximate a differentiable function, using the Sobolev norm <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,infin</sup> to any desired accuracy. This approximation result is combined with the second-order Taylor series expansion to show that polynomial Lyapunov functions can approximate continuous Lyapunov functions arbitrarily well on bounded sets. The investigation is motivated by the use of polynomial optimization algorithms to construct polynomial Lyapunov functions.

Stability Analysis of Implicit Polynomial Systems

This technical note develops a robust stability analysis method for a class of implicit polynomial systems subject to uncertain constant parameters. A polynomial (with respect to the state and uncertain parameters) Lyapunov function is computed to assess the robust local stability of the system and to provide a robust estimate of the domain of attraction. The results are given in terms of linear matrix inequalities that are affinely dependent on the system state and uncertain parameters. Numerical examples illustrate the effectiveness of the developed approach.

Guaranteed Cost Control of Polynomial Fuzzy Systems via a Sum of Squares Approach

This paper presents the guaranteed cost control of polynomial fuzzy systems via a sum of squares (SOS) approach. First, we present a polynomial fuzzy model and controller that are more general representations of the well-known Takagi-Sugeno (T-S) fuzzy model and controller, respectively. Second, we derive a guaranteed cost control design condition based on polynomial Lyapunov functions. Hence, the design approach discussed in this paper is more general than the existing LMI approaches (to T-S fuzzy control system designs) based on quadratic Lyapunov functions. The design condition realizes a guaranteed cost control by minimizing the upper bound of a given performance function. In addition, the design condition in the proposed approach can be represented in terms of SOS and is numerically (partially symbolically) solved via the recent developed SOSTOOLS. To illustrate the validity of the design approach, two design examples are provided. The first example deals with a complicated nonlinear system. The second example presents micro helicopter control. Both the examples show that our approach provides more extensive design results for the existing LMI approach.

Establishing Convexity of Polynomial Lyapunov Functions and Their Sublevel Sets

There has been renewed interest in the use of polynomial and homogeneous polynomial Lyapunov functions for addressing stability and performance issues in systems and control theory. However, no condition is yet available to establish the convexity of these functions and the convexity of their sublevel sets, properties that can be useful in many applications. In this paper, new conditions for establishing these convexity properties are proposed, which can be handled through convex linear matrix inequalities optimizations by means of sum-of-squares relaxations.

Deconvolution filtering for stochastic systems via homogeneous polynomial Lyapunov functions

This paper deals with the robust H ∞ and L 2 – L ∞ deconvolution filtering problems for stochastic systems with polytopic uncertainties. The purpose is to design a full-order deconvolution filter such that (i) the deconvolution error system is robustly exponentially mean-square stable with a prescribed decay rate and (ii) an H ∞ or L 2 – L ∞ performance of the deconvolution error system is guaranteed. Based on a homogeneous polynomial parameter-dependent matrix (HPPDM) approach, sufficient conditions for the solvability of these problems are given in terms of linear matrix inequalities (LMIs). Such conditions are dependent on the decay rate, which enables one to design robust deconvolution filters by selecting the decay rates according to different practical conditions. In addition, when these LMIs are feasible, a design procedure of the desired filters is developed and an exponential estimate for the deconvolution error system is given. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed design methods.

Stability analysis of uncertain genetic sum regulatory networks

This paper addresses the problem of establishing robust stability of uncertain genetic networks with sum regulatory functions. Specifically, we first consider uncertain genetic networks where the regulation occurs at the transcriptional level, and we derive a sufficient condition for robust stability by introducing a bounding set of the uncertain nonlinearity. We hence show that this condition can be formulated as a convex optimization through polynomial Lyapunov functions and polynomial descriptions of the bounding set by exploiting the square matricial representation (SMR) of polynomials which allows to establish whether a polynomial is a sum of squares (SOS) via a linear matrix inequality (LMI). Then, we propose a method for computing a family of bounding sets by means of convex optimizations. It is worthwhile to remark that these results are derived in spite of the fact that the variable equilibrium point cannot be computed as being the solution of a system of parameter-dependent nonlinear equations, and is hence unknown. Lastly, the proposed approach is extended to models where the regulation occurs at different levels and both mRNA and protein dynamics are nonlinear.

Stability Region Analysis Using Polynomial and Composite Polynomial Lyapunov Functions and Sum-of-Squares Programming

We propose using (bilinear) sum-of-squares programming for obtaining inner bounds of RoAs for dynamical systems with polynomial vector fields. We search for polynomial as well as composite Lyapunov functions comprised of pointwise maximums of polynomial functions. Results for several examples from the literature are presented using the proposed methods and the PENBMI solver.